A microfluidic device for high density hydrodynamic cell trapping, growth and super-resolution imaging

Super-Resolution imaging techniques such as Fluorescent Photo-Activation Localisation Microscopy (FPALM) have created a powerful new toolkit for investigating living cells, however a simple platform for growing, trapping, holding and controlling the cells is needed before the approach can become truly widespread. We present a microfluidic device formed in polydimethylsiloxane (PDMS) with a fluidic design which traps cells in a high-density array of wells and holds them very still throughout the life cycle, using hydrodynamic forces only. The device meets or exceeds all the necessary criteria for FPALM imaging of Schizosaccharomyces pombe and is designed to remain flexible, robust and easy to use. © 2011 IEEE.

Discrete dislocation plasticity analysis of size effects in single crystals

The effect of size and slip system configuration on the tensile stress-strain response of micron-sized planar crystals as obtained from discrete dislocation plasticity simulations is presented. The crystals are oriented for either single or symmetric double slip. With the rotation of the tensile axis unconstrained, there is a strong size dependence, with the flow strength increasing with decreasing specimen size. Below a certain specimen size, the flow strength of the crystals is set by the nucleation strength of the initially present Frank-Read sources. The main features of the size dependence are the same for both the single and symmetric double slip configurations.

Imaging the femoral cortex: thickness, density and mass from clinical CT.

There is growing evidence that focal thinning of cortical bone in the proximal femur may predispose a hip to fracture. Detecting such defects in clinical CT is challenging, since cortices may be significantly thinner than the imaging system's point spread function. We recently proposed a model-fitting technique to measure sub-millimetre cortices, an ill-posed problem which was regularized by assuming a specific, fixed value for the cortical density. In this paper, we develop the work further by proposing and evaluating a more rigorous method for estimating the constant cortical density, and extend the paradigm to encompass the mapping of cortical mass (mineral mg/cm(2)) in addition to thickness. Density, thickness and mass estimates are evaluated on sixteen cadaveric femurs, with high resolution measurements from a micro-CT scanner providing the gold standard. The results demonstrate robust, accurate measurement of peak cortical density and cortical mass. Cortical thickness errors are confined to regions of thin cortex and are bounded by the extent to which the local density deviates from the peak, averaging 20% for 0.5mm cortex.

Growth of ultrahigh density single-walled carbon nanotube forests by improved catalyst design.

We have grown vertically aligned single-walled carbon nanotube forests with an area density of 1.5 × 10(13) cm(-2), the highest yet achieved, by reducing the average diameter of the nanotubes. We use a nanolaminate Fe-Al(2)O(3) catalyst design consisting of three layers of Al(2)O(3), Fe, and Al(2)O(3), in which the lower Al(2)O(3) layer is densified by an oxygen plasma treatment to increase its diffusion barrier properties, to allow a thinner catalyst layer to be used. This high nanotube density is desirable for using carbon nanotubes as interconnects in integrated circuits.

The Gaussian process density sampler

We present the Gaussian Process Density Sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Samples drawn from the GPDS are consistent with exact, independent samples from a fixed density function that is a transformation of a function drawn from a Gaussian process prior. Our formulation allows us to infer an unknown density from data using Markov chain Monte Carlo, which gives samples from the posterior distribution over density functions and from the predictive distribution on data space. We can also infer the hyperparameters of the Gaussian process. We compare this density modeling technique to several existing techniques on a toy problem and a skullreconstruction task.