University of St Andrews

About: University of St Andrews is a education organization based out in St Andrews, Fife, United Kingdom. It is known for research contribution in the topics: Population & Laser. The organization has 16260 authors who have published 43364 publications receiving 1636072 citations. The organization is also known as: St Andrews University & University of St. Andrews.

Bycatch of marine mammals in U.S. and global fisheries.

Abstract: Fisheries bycatch poses a significant threat to many populations of marine mammals, but there are few published estimates of the magnitude of these catches. We estimated marine mammal bycatch in U.S. fisheries from 1990 to 1999 with data taken from the stock assessment reports required by the U.S. Marine Mammal Protection Act. The mean annual bycatch of marine mammals during this period was 6215 ± 448 (SE). Bycatch of cetaceans and pinnipeds occurred in similar numbers. Most cetacean (84%) and pinniped (98%) bycatch occurred in gill-net fisheries. Marine mammal bycatch declined significantly over the decade, primarily because of a reduction in the bycatch of cetaceans. Total marine mammal bycatch was significantly lower after the implementation of take reduction measures in the latter half of the decade. We derived a crude first estimate of marine mammal bycatch in the world's fisheries by expanding U.S. bycatch with data on fleet composition from the Food and Agriculture Organization. The global bycatch of marine mammals is in the hundreds of thousands. Bycatch is likely to have significant demographic effects on many populations of marine mammals. Better data are urgently needed to fully understand the impact of these interactions.

The Emergence of a Lanthanide-Rich Kilonova Following the Merger of Two Neutron Stars

Abstract: We report the discovery and monitoring of the near-infrared counterpart (AT2017gfo) of a binary neutron-star merger event detected as a gravitational wave source by Advanced Laser Interferometer Gravitational-wave Observatory (LIGO)/Virgo (GW170817) and as a short gamma-ray burst by Fermi Gamma-ray Burst Monitor (GBM) and Integral SPI-ACS (GRB 170817A). The evolution of the transient light is consistent with predictions for the behavior of a "kilonova/macronova" powered by the radioactive decay of massive neutron-rich nuclides created via r-process nucleosynthesis in the neutron-star ejecta. In particular, evidence for this scenario is found from broad features seen in Hubble Space Telescope infrared spectroscopy, similar to those predicted for lanthanide-dominated ejecta, and the much slower evolution in the near-infrared ${K}_{{\rm{s}}}$-band compared to the optical. This indicates that the late-time light is dominated by high-opacity lanthanide-rich ejecta, suggesting nucleosynthesis to the third r-process peak (atomic masses $A\approx 195$). This discovery confirms that neutron-star mergers produce kilo-/macronovae and that they are at least a major—if not the dominant—site of rapid neutron capture nucleosynthesis in the universe.

A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking

Abstract: Motivation: Accurately predicting the binding affinities of large sets of diverse protein–ligand complexes is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for analysing the outputs of molecular docking, which in turn is an important technique for drug discovery, chemical biology and structural biology. Each scoring function assumes a predetermined theory-inspired functional form for the relationship between the variables that characterize the complex, which also include parameters fitted to experimental or simulation data and its predicted binding affinity. The inherent problem of this rigid approach is that it leads to poor predictivity for those complexes that do not conform to the modelling assumptions. Moreover, resampling strategies, such as cross-validation or bootstrapping, are still not systematically used to guard against the overfitting of calibration data in parameter estimation for scoring functions. Results: We propose a novel scoring function (RF-Score) that circumvents the need for problematic modelling assumptions via non-parametric machine learning. In particular, Random Forest was used to implicitly capture binding effects that are hard to model explicitly. RF-Score is compared with the state of the art on the demanding PDBbind benchmark. Results show that RF-Score is a very competitive scoring function. Importantly, RF-Score's performance was shown to improve dramatically with training set size and hence the future availability of more high-quality structural and interaction data is expected to lead to improved versions of RF-Score. Contact:pedro.ballester@ebi.ac.uk; jbom@st-andrews.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Competitive accretion in embedded stellar clusters

Abstract: We investigate the physics of gas accretion in young stellar clusters. Accretion in clusters is a dynamic phenomenon as both the stars and the gas respond to the same gravitational potential. Accretion rates are highly non-uniform with stars nearer the centre of the cluster, where gas densities are higher, accreting more than others. This competitive accretion naturally results in both initial mass segregation and a spectrum of stellar masses. Accretion in gas-dominated clusters is well modelled using a tidal-lobe radius instead of the commonly used Bondi–Hoyle accretion radius. This works as both the stellar and gas velocities are under the influence of the same gravitational potential and are thus comparable. The low relative velocity which results means that Rtidal