The ability to spatially control cellular adhesion in a continuous manner on a biocompatible substrate is an important factor in designing new biomaterials for use in wound healing and tissue engineering applications. In this work, a novel method of engineering cell-adhesive RGD-ligand density gradients to control specific cell adhesion across a substrate is presented. Polymer brushes exhibiting spatially defined gradients in chain density are created and subsequently functionalized with RGD to create ligand density gradients capable of inducing cell adhesion on an otherwise weakly adhesive substrate. Cell studies indicate that these ligand-functionalized surfaces are noncytotoxic, with cellular adhesion increasing with RGD-ligand density across the gradient brush surface.

Building trust in science and evidence-based decision-making depends heavily on the credibility of studies and their findings. Researchers employ many different study designs that vary in their risk of bias to evaluate the true effect of interventions or impacts. Here, we empirically quantify, on a large scale, the prevalence of different study designs and the magnitude of bias in their estimates. Randomised designs and controlled observational designs with pre-intervention sampling were used by just 23% of intervention studies in biodiversity conservation, and 36% of intervention studies in social science. We demonstrate, through pairwise within-study comparisons across 49 environmental datasets, that these types of designs usually give less biased estimates than simpler observational designs. We propose a model-based approach to combine study estimates that may suffer from different levels of study design bias, discuss the implications for evidence synthesis, and how to facilitate the use of more credible study designs.

No abstract

No abstract

Identifying individual animals is crucial for many biological investigations. In response to some of the limitations of current identification methods, new automated computer vision approaches have emerged with strong performance. Here, we review current advances of computer vision identification techniques to provide both computer scientists and biologists with an overview of the available tools and discuss their applications. We conclude by offering recommendations for starting an animal identification project, illustrate current limitations and propose how they might be addressed in the future.