Modeling the influence of Twitter in reducing and increasing the spread of influenza epidemics

In this paper we present compartmentalized neuron arraying (CNA) microfluidic circuits for the preparation of neuronal networks using minimal cellular inputs (10–100-fold less than existing systems). The approach combines the benefits of microfluidics for precision single cell handling with biomaterial patterning for the long term maintenance of neuronal arrangements. A differential flow principle was used for cell metering and loading along linear arrays. An innovative water masking technique was developed for the inclusion of aligned biomaterial patterns within the microfluidic environment. For patterning primary neurons the technique involved the use of meniscus-pinning micropillars to align a water mask for plasma stencilling a poly-amine coating. The approach was extended for patterning the human SH-SY5Y neuroblastoma cell line using a poly(ethylene glycol) (PEG) back-fill and for dopaminergic LUHMES neuronal precursors by the further addition of a fibronectin coating. The patterning efficiency Epatt was >75% during lengthy in chip culture, with ~85% of the outgrowth channels occupied by neurites. Neurons were also cultured in next generation circuits which enable neurite guidance into all outgrowth channels for the formation of extensive inter-compartment networks. Fluidic isolation protocols were developed for the rapid and sustained treatment of the different cellular and sub-cellular compartments. In summary, this research demonstrates widely applicable microfluidic methods for the construction of compartmentalized brain models with single cell precision. These minimalistic ex vivo tissue constructs pave the way for high throughput experimentation to gain deeper insights into pathological processes such as Alzheimer and Parkinson Diseases, as well as neuronal development and function in health

Potential of Electrical Resistivity Tomography to Infer Aquifer Transport Characteristics from Tracer Studies. A Synthetic Case Study

[ 1] With time-lapse electrical resistivity tomography ( ERT), transport processes in the subsurface can be imaged and monitored. The information content of obtained spatiotemporal data sets opens new ways to characterize subsurface spatial variability. Difficulties regarding a quantitative interpretation of the imaged transport may arise from the fact that data inversion used in ERT is generally underdetermined and that ERT data acquisition is often limited to two-dimensional ( 2-D) image planes. To address this problem, we conducted a numerical tracer experiment in a synthetic heterogeneous aquifer with preset variability and spatial correlation of the hydraulic conductivity and monitored the tracer breakthrough in a 2-D image plane perpendicular to the mean flow direction using time-lapse ERT. The tracer breakthrough patterns in the image plane were interpreted using equivalent transport models: an equivalent convection dispersion equation to characterize the spatially averaged breakthrough and a stream tube model to characterize local breakthrough curves. Equivalent parameters derived from simulated and from ERT inverted concentrations showed a good agreement, which demonstrates the potential of ERT to characterize subsurface transport. Using first-order approximate solutions of stochastic flow and transport equations, equivalent model parameters and their spatial variability were interpreted in terms of the local-scale dispersion and the spatial variability of the hydraulic conductivity. The spatial correlations of the stream tube velocity and of the hydraulic conductivity were found to be closely related. Consequently, the hydraulic conductivity spatial correlation may be inferred from stream tube velocity distributions, which can be observed with sufficiently high spatial resolution using ERT