Climate impact and adaptation to heat and drought stress of regional and global wheat production

Wheat (Triticum aestivum) is the most widely grown food crop in the world threatened by future climate change. In this study, we simulated climate change impacts and adaptation strategies for wheat globally using new crop genetic traits (CGT), including increased heat tolerance, early vigor to increase early crop water use, late flowering to reverse an earlier anthesis in warmer conditions, and the combined traits with additional nitrogen (N) fertilizer applications, as an option to maximize genetic gains. These simulations were completed using three wheat crop models and five Global Climate Models (GCM) for RCP 8.5 at mid-century. Crop simulations were compared with country, US state, and US county grain yield and production. Wheat yield and production from high-yielding and low-yielding countries were mostly captured by the model ensemble mean. However, US state and county yields and production were often poorly reproduced, with large variability in the models, which is likely due to poor soil and crop management input data at this scale. Climate change is projected to decrease global wheat production by −1.9% by mid-century. However, the most negative impacts are projected to affect developing countries in tropical regions. The model ensemble mean suggests large negative yield impacts for African and Southern Asian countries where food security is already a problem. Yields are predicted to decline by −15% in African countries and −16% in Southern Asian countries by 2050. Introducing CGT as an adaptation to climate change improved wheat yield in many regions, but due to poor nutrient management, many developing countries only benefited from adaptation from CGT when combined with additional N fertilizer. As growing conditions and the impact from climate change on wheat vary across the globe, region-specific adaptation strategies need to be explored to increase the possible benefits of adaptations to climate change in the future.info:eu-repo/semantics/publishedVersio

Deep Learning Criminal Networks

Recent advances in deep learning methods have enabled researchers to develop and apply algorithms for the analysis and modeling of complex networks. These advances have sparked a surge of interest at the interface between network science and machine learning. Despite this, the use of machine learning methods to investigate criminal networks remains surprisingly scarce. Here, we explore the potential of graph convolutional networks to learn patterns among networked criminals and to predict various properties of criminal networks. Using empirical data from political corruption, criminal police intelligence, and criminal financial networks, we develop a series of deep learning models based on the GraphSAGE framework that are capable to recover missing criminal partnerships, distinguish among types of associations, predict the amount of money exchanged among criminal agents, and even anticipate partnerships and recidivism of criminals during the growth dynamics of corruption networks, all with impressive accuracy. Our deep learning models significantly outperform previous shallow learning approaches and produce high-quality embeddings for node and edge properties. Moreover, these models inherit all the advantages of the GraphSAGE framework, including the generalization to unseen nodes and scaling up to large graph structures.Comment: 14 two-column pages, 5 figure

The lectin-specific activity of Toxoplasma gondii microneme proteins 1 and 4 binds Toll-like receptor 2 and 4 N-glycans to regulate innate immune priming.

Infection of host cells by Toxoplasma gondii is an active process, which is regulated by secretion of microneme (MICs) and rhoptry proteins (ROPs and RONs) from specialized organelles in the apical pole of the parasite. MIC1, MIC4 and MIC6 assemble into an adhesin complex secreted on the parasite surface that functions to promote infection competency. MIC1 and MIC4 are known to bind terminal sialic acid residues and galactose residues, respectively and to induce IL-12 production from splenocytes. Here we show that rMIC1- and rMIC4-stimulated dendritic cells and macrophages produce proinflammatory cytokines, and they do so by engaging TLR2 and TLR4. This process depends on sugar recognition, since point mutations in the carbohydrate-recognition domains (CRD) of rMIC1 and rMIC4 inhibit innate immune cells activation. HEK cells transfected with TLR2 glycomutants were selectively unresponsive to MICs. Following in vitro infection, parasites lacking MIC1 or MIC4, as well as expressing MIC proteins with point mutations in their CRD, failed to induce wild-type (WT) levels of IL-12 secretion by innate immune cells. However, only MIC1 was shown to impact systemic levels of IL-12 and IFN-γ in vivo. Together, our data show that MIC1 and MIC4 interact physically with TLR2 and TLR4 N-glycans to trigger IL-12 responses, and MIC1 is playing a significant role in vivo by altering T. gondii infection competency and murine pathogenesis

Study of elementary reactions with the hades dielectron spectrometer

Results obtained with the HADES dielectron spectrometer at GSI are discussed, with emphasis on dilepton production in elementary reactions.Díaz Medina, José, [email protected] ; Gil Ortiz, Alejandro, [email protected]

Energy Estimation of Cosmic Rays with the Engineering Radio Array of the Pierre Auger Observatory

The Auger Engineering Radio Array (AERA) is part of the Pierre Auger Observatory and is used to detect the radio emission of cosmic-ray air showers. These observations are compared to the data of the surface detector stations of the Observatory, which provide well-calibrated information on the cosmic-ray energies and arrival directions. The response of the radio stations in the 30 to 80 MHz regime has been thoroughly calibrated to enable the reconstruction of the incoming electric field. For the latter, the energy deposit per area is determined from the radio pulses at each observer position and is interpolated using a two-dimensional function that takes into account signal asymmetries due to interference between the geomagnetic and charge-excess emission components. The spatial integral over the signal distribution gives a direct measurement of the energy transferred from the primary cosmic ray into radio emission in the AERA frequency range. We measure 15.8 MeV of radiation energy for a 1 EeV air shower arriving perpendicularly to the geomagnetic field. This radiation energy -- corrected for geometrical effects -- is used as a cosmic-ray energy estimator. Performing an absolute energy calibration against the surface-detector information, we observe that this radio-energy estimator scales quadratically with the cosmic-ray energy as expected for coherent emission. We find an energy resolution of the radio reconstruction of 22% for the data set and 17% for a high-quality subset containing only events with at least five radio stations with signal.Comment: Replaced with published version. Added journal reference and DO

Measurement of the Radiation Energy in the Radio Signal of Extensive Air Showers as a Universal Estimator of Cosmic-Ray Energy

We measure the energy emitted by extensive air showers in the form of radio emission in the frequency range from 30 to 80 MHz. Exploiting the accurate energy scale of the Pierre Auger Observatory, we obtain a radiation energy of 15.8 \pm 0.7 (stat) \pm 6.7 (sys) MeV for cosmic rays with an energy of 1 EeV arriving perpendicularly to a geomagnetic field of 0.24 G, scaling quadratically with the cosmic-ray energy. A comparison with predictions from state-of-the-art first-principle calculations shows agreement with our measurement. The radiation energy provides direct access to the calorimetric energy in the electromagnetic cascade of extensive air showers. Comparison with our result thus allows the direct calibration of any cosmic-ray radio detector against the well-established energy scale of the Pierre Auger Observatory.Comment: Replaced with published version. Added journal reference and DOI. Supplemental material in the ancillary file

Measurement of the cosmic ray spectrum above 4×10184{\times}10^{18} eV using inclined events detected with the Pierre Auger Observatory

A measurement of the cosmic-ray spectrum for energies exceeding $4{\times}10^{18}$ eV is presented, which is based on the analysis of showers with zenith angles greater than $60^{\circ}$ detected with the Pierre Auger Observatory between 1 January 2004 and 31 December 2013. The measured spectrum confirms a flux suppression at the highest energies. Above $5.3{\times}10^{18}$ eV, the "ankle", the flux can be described by a power law $E^{-\gamma}$ with index $\gamma=2.70 \pm 0.02 \,\text{(stat)} \pm 0.1\,\text{(sys)}$ followed by a smooth suppression region. For the energy ($E_\text{s}$) at which the spectral flux has fallen to one-half of its extrapolated value in the absence of suppression, we find $E_\text{s}=(5.12\pm0.25\,\text{(stat)}^{+1.0}_{-1.2}\,\text{(sys)}){\times}10^{19}$ eV.Comment: Replaced with published version. Added journal reference and DO