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.

Microfluidic sorting in an optical lattice

Abstract: The response of a microscopic dielectric object to an applied light field can profoundly affect its kinetic motion1. A classic example of this is an optical trap, which can hold a particle in a tightly focused light beam2. Optical fields can also be used to arrange, guide or deflect particles in appropriate light-field geometries3,4. Here we demonstrate an optical sorter for microscopic particles that exploits the interaction of particles—biological or otherwise—with an extended, interlinked, dynamically reconfigurable, three-dimensional optical lattice. The strength of this interaction with the lattice sites depends on the optical polarizability of the particles, giving tunable selection criteria. We demonstrate both sorting by size (of protein microcapsule drug delivery agents) and sorting by refractive index (of other colloidal particle streams). The sorting efficiency of this method approaches 100%, with values of 96% or more observed even for concentrated solutions with throughputs exceeding those reported for fluorescence-activated cell sorting5. This powerful, non-invasive technique is suited to sorting and fractionation within integrated (‘lab-on-a-chip’) microfluidic systems, and can be applied in colloidal, molecular and biological research.

Social learning strategies.

Abstract: In most studies of social learning in animals, no attempt has been made to examine the nature of the strategy adopted by animals when they copy others. Researchers have expended considerable effort in exploring the psychological processes that underlie social learning and amassed extensive data banks recording purported social learning in the field, but the contexts under which animals copy others remain unexplored. Yet, theoretical models used to investigate the adaptive advantages of social learning lead to the conclusion that social learning cannot be indiscriminate and that individuals should adopt strategies that dictate the circumstances under which they copy others and from whom they learn. In this article, I discuss a number of possible strategies that are predicted by theoretical analyses, including copy when uncertain, copy the majority, and copy if better, and consider the empirical evidence in support of each, drawing from both the animal and human social learning literature. Reliance on social learning strategies may be organized hierarchically, their being employed by animals when unlearned and asocially learned strategies prove ineffective but before animals take recourse in innovation.

Species abundance distributions: moving beyond single prediction theories to integration within an ecological framework

Abstract: Species abundance distributions (SADs) follow one of ecologys oldest and most universal laws – every community shows a hollow curve or hyperbolic shape on a histogram with many rare species and just a few common species. Here, we review theoretical, empirical and statistical developments in the study of SADs. Several key points emerge. (i) Literally dozens of models have been proposed to explain the hollow curve. Unfortunately, very few models are ever rejected, primarily because few theories make any predictions beyond the hollow-curve SAD itself. (ii) Interesting work has been performed both empirically and theoretically, which goes beyond the hollow-curve prediction to provide a rich variety of information about how SADs behave. These include the study of SADs along environmental gradients and theories that integrate SADs with other biodiversity patterns. Central to this body of work is an effort to move beyond treating the SAD in isolation and to integrate the SAD into its ecological context to enable making many predictions. (iii) Moving forward will entail understanding how sampling and scale affect SADs and developing statistical tools for describing and comparing SADs. We are optimistic that SADs can provide significant insights into basic and applied ecological science.