Transverse lattice calculation of the pion light-cone wavefunctions

We calculate the light-cone wavefunctions of the pion by solving the meson boundstate problem in a coarse transverse lattice gauge theory using DLCQ. A large-N_c approximation is made and the light-cone Hamiltonian expanded in massive dynamical fields at fixed lattice spacing. In contrast to earlier calculations, we include contributions from states containing many gluonic link-fields between the quarks.The Hamiltonian is renormalised by a combination of covariance conditions on boundstates and fitting the physical masses M_rho and M_pi, decay constant f_pi, and the string tension sigma. Good covariance is obtained for the lightest 0^{-+} state, which we identify with the pion. Many observables can be deduced from its light-cone wavefunctions.After perturbative evolution,the quark valence structure function is found to be consistent with the experimental structure function deduced from Drell-Yan pi-nucleon data in the valence region x > 0.5. In addition, the pion distribution amplitude is consistent with the experimental distribution deduced from the pi gamma^* gamma transition form factor and diffractive dissociation. A new observable we calculate is the probability for quark helicity correlation. We find a 45% probability that the valence-quark helicities are aligned in the pion.Comment: 27 pages, 9 figure

The SAMI Galaxy Survey: Spatially resolving the environmental quenching of star formation in GAMA galaxies

We use data from the Sydney-AAO Multi-Object Integral Field Spectrograph (SAMI) Galaxy Survey and the Galaxy And Mass Assembly (GAMA) survey to investigate the spatially-resolved signatures of the environmental quenching of star formation in galaxies. Using dust-corrected measurements of the distribution of Hα emission we measure the radial profiles of star formation in a sample of 201 star-forming galaxies covering three orders of magnitude in stellar mass (M∗M∗; 108.1-1010.95 M⊙) and in 5th nearest neighbour local environment density (Σ5; 10−1.3- 102.1 Mpc−2). We show that star formation rate gradients in galaxies are steeper in dense (log10(Σ5/Mpc2) > 0.5) environments by 0.58 ± 0.29 dex re−1 in galaxies with stellar masses in the range 1010 1.0). These lines of evidence strongly suggest that with increasing local environment density the star formation in galaxies is suppressed, and that this starts in their outskirts such that quenching occurs in an outside-in fashion in dense environments and is not instantaneous

The LEGA-C and SAMI galaxy surveys: quiescent stellar populations and the mass-size plane across 6 Gyr

Galaxie

The SAMI Galaxy Survey: Data Release Two with absorption-line physics value-added products

Instrumentatio

The SAMI Galaxy Survey: Data Release One with emission-line physics value-added products

We present the first major release of data from the SAMI Galaxy Survey. This data release focuses on the emission-line physics of galaxies. Data Release One includes data for 772 galaxies, about 20 per cent of the full survey. Galaxies included have the redshift range 0.004 < z < 0.092, a large mass range (7.6 < logM*/M⊙ < 11.6), and star formation rates of ~10-4 to ~101M⊙ yr-1. For each galaxy, we include two spectral cubes and a set of spatially resolved 2D maps: single- and multi-component emission-line fits (with dust-extinction corrections for strong lines), local dust extinction, and star formation rate. Calibration of the fibre throughputs, fluxes, and differential atmospheric refraction has been improved over the Early Data Release. The data have average spatial resolution of 2.16 arcsec (full width at half-maximum) over the 15 arcsec diameter field of view and spectral (kinematic) resolution of R = 4263 (σ = 30 km s-1) around Ha. The relative flux calibration is better than 5 per cent, and absolute flux calibration has an rms of 10 per cent. The data are presented online through the Australian Astronomical Observatory's Data Central

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Strong floristic distinctiveness across Neotropical successional forests

Forests that regrow naturally on abandoned fields are important for restoring biodiversity and ecosystem services, but can they also preserve the distinct regional tree floras? Using the floristic composition of 1215 early successional forests (≤20 years) in 75 human-modified landscapes across the Neotropic realm, we identified 14 distinct floristic groups, with a between-group dissimilarity of 0.97. Floristic groups were associated with location, bioregions, soil pH, temperature seasonality, and water availability. Hence, there is large continental-scale variation in the species composition of early successional forests, which is mainly associated with biogeographic and environmental factors but not with human disturbance indicators. This floristic distinctiveness is partially driven by regionally restricted species belonging to widespread genera. Early secondary forests contribute therefore to restoring and conserving the distinctiveness of bioregions across the Neotropical realm, and forest restoration initiatives should use local species to assure that these distinct floras are maintained

WHO global research priorities for antimicrobial resistance in human health

The WHO research agenda for antimicrobial resistance (AMR) in human health has identified 40 research priorities to be addressed by the year 2030. These priorities focus on bacterial and fungal pathogens of crucial importance in addressing AMR, including drug-resistant pathogens causing tuberculosis. These research priorities encompass the entire people-centred journey, covering prevention, diagnosis, and treatment of antimicrobial-resistant infections, in addition to addressing the overarching knowledge gaps in AMR epidemiology, burden and drivers, policies and regulations, and awareness and education. The research priorities were identified through a multistage process, starting with a comprehensive scoping review of knowledge gaps, with expert inputs gathered through a survey and open call. The priority setting involved a rigorous modified Child Health and Nutrition Research Initiative approach, ensuring global representation and applicability of the findings. The ultimate goal of this research agenda is to encourage research and investment in the generation of evidence to better understand AMR dynamics and facilitate policy translation for reducing the burden and consequences of AMR