Does Aid Promote Electoral Integrity?

Since the late 1990s, aid spending for elections has witnessed a dramatic increase. Yet, we lack a comprehensive evaluation of aid effectiveness in this particular programme area. Here, we investigate the impact of aid on electoral integrity using panel data on purpose-disaggregated aid disbursements and a multi-dimensional index of electoral quality from the Varieties of Democracy project. Based on 502 elections in 126 aid-receiving countries during 2002–2015, we estimate a statistically significant effect of election-support ODA on the integrity of elections. The estimated effect is, however, economically small and not very persistent. In the long run, a permanent increase in aid spending by one million US$ leads to an improvement in electoral quality of 1.4 per cent of a standard deviation on the integrity index. We also find that different dimensions of electoral integrity are variably responsive to donor interventions. Additionally, aid spending for elections is subject to diminishing marginal returns, and is less effective at higher levels of development. These findings underline the difficulty of promoting democratic change in countries with adverse structural conditions. Still, donors may improve the cost-effectiveness of electoral assistance programmes by targeting specific countries and prioritising certain types of intervention

A general theory of DNA-mediated and other valence-limited interactions

We present a general theory for predicting the interaction potentials between DNA-coated colloids, and more broadly, any particles that interact via valence-limited ligand-receptor binding. Our theory correctly incorporates the configurational and combinatorial entropic factors that play a key role in valence-limited interactions. By rigorously enforcing self-consistency, it achieves near-quantitative accuracy with respect to detailed Monte Carlo calculations. With suitable approximations and in particular geometries, our theory reduces to previous successful treatments, which are now united in a common and extensible framework. We expect our tools to be useful to other researchers investigating ligand-mediated interactions. A complete and well-documented Python implementation is freely available at http://github.com/patvarilly/DNACC .Comment: 18 pages, 10 figure

Bimodal brush-functionalized nanoparticles selective to receptor surface density.

Nanoparticles or drug carriers which can selectively bind to cells expressing receptors above a certain threshold surface density are very promising for targeting cells overexpressing specific receptors under pathological conditions. Simulations and theoretical studies have suggested that such selectivity can be enhanced by functionalizing nanoparticles with a bimodal polymer monolayer (BM) containing shorter ligated chains and longer inert protective chains. However, a systematic study of the effect of these parameters under tightly controlled conditions is still missing. Here, we develop well-defined and highly specific platforms mimicking particle-cell interface using surface chemistry to provide a experimental proof of such selectivity. Using surface plasmon resonance and atomic force microscopy, we report the selective adsorption of BM-functionalized nanoparticles, and especially, a significant enhanced selective behavior by using a BM with longer protective chains. Furthermore, a model is also developed to describe the repulsive contribution of the protective brush to nanoparticle adsorption. This model is combined with super-selectivity theory to support experimental findings and shows that the observed selectivity is due to the steric energy barrier which requires a high number of ligand-receptor bonds to allow nanoparticle adsorption. Finally, the results show how the relative length and molar ratio of two chains can be tuned to target a threshold surface density of receptors and thus lay the foundation for the rational design of BM-functionalized nanoparticles for selective targeting

Landmark Optimization Using Local Curvature for Point-Based Nonlinear Rodent Brain Image Registration

Purpose. To develop a technique to automate landmark selection for point-based interpolating transformations for nonlinear medical image registration. Materials and Methods. Interpolating transformations were calculated from homologous point landmarks on the source (image to be transformed) and target (reference image). Point landmarks are placed at regular intervals on contours of anatomical features, and their positions are optimized along the contour surface by a function composed of curvature similarity and displacements of the homologous landmarks. The method was evaluated in two cases (n = 5 each). In one, MRI was registered to histological sections; in the second, geometric distortions in EPI MRI were corrected. Normalized mutual information and target registration error were calculated to compare the registration accuracy of the automatically and manually generated landmarks. Results. Statistical analyses demonstrated significant improvement (P < 0.05) in registration accuracy by landmark optimization in most data sets and trends towards improvement (P < 0.1) in others as compared to manual landmark selection