Self-assembly of DNA-coded nanoclusters

We present a theoretical discussion of a self-assembly scheme which makes it possible to use DNA to uniquely encode the composition and structure of micro- and nanoparticle clusters. These anisotropic DNA-decorated clusters can be further used as building blocks for hierarchical self-assembly of larger structures. We address several important aspects of possible experimental implementation of the proposed scheme: the competition between different types of clusters in a solution, possible jamming in an unwanted configuration, and the degeneracy due to symmetry with respect to particle permutations.Comment: v2, 4 pages, 7 figures, added journal re

The first passage problem for diffusion through a cylindrical pore with sticky walls

We calculate the first passage time distribution for diffusion through a cylindrical pore with sticky walls. A particle diffusively explores the interior of the pore through a series of binding and unbinding events with the cylinder wall. Through a diagrammatic expansion we obtain first passage time statistics for the particle's exit from the pore. Connections between the model and nucleocytoplasmic transport in cells are discussed.Comment: v2: 13 pages, 6 figures, substantial revision

Revealing the visually unknown in ancient manuscripts with a similarity measure for IR-imaged inks

One of the tasks facing historians and conservationists is the authentication or dating of medieval manuscripts. To this end it is important to them to verify whether writings on the same or different manuscripts are concurrent. In this work we explore this task by capturing images of manuscript pages in infrared (IR) and modelling and then comparing the ink appearance of segmented text. The modelling of the text appearance relies on the unsupervised multimodal clustering of ink descriptors and the derived probability density functions. The similarity measure is built around the distribution of cluster labels and their proportions. We demonstrate our method by using both model inks of known composition and authentic Byzantine manuscripts

An ink texture descriptor for nir-imaged medieval documents

In this work we explore the task of authenticating and dating ancient manuscripts by capturing images of pages in nearinfrared (NIR) and modelling and then comparing the ink appearance of segmented text. We present a texture feature descriptor to characterize and recognize semi-transparent materials such as the inks found in manuscripts. These textural patterns are different in nature from perceptual entities such as textons, tokens, frequency or repeatability of textural elements. Our ink texture descriptor relates a set of ink features from various first and second-order statistics to semi-liquid and viscous image-based properties of inks. In particular, we propose eigen features from the joint gray-level probabilities and off-diagonal sums of co-occurrence matrices. We test the qualities of the features with a classifier trained with the ink descriptor to show how well it recognizes eight different inks of known composition. Presented with the very same task the human visual system would fail to spot the ink composition difference given the inks inter-class and intra-class distances are extremely short