Primary care management of Long-Term opioid therapy.

The United States underwent massive expansion in opioid prescribing from 1990-2010, followed by opioid stewardship initiatives and reduced prescribing. Opioids are no longer considered first-line therapy for most chronic pain conditions and clinicians should first seek alternatives in most circumstances. Patients who have been treated with opioids long-term should be managed differently, sometimes even continued on opioids due to physiologic changes wrought by long-term opioid therapy and documented risks of discontinuation.

Routing Brain Traffic Through the Von Neumann Bottleneck: Parallel Sorting and Refactoring.

Generic simulation code for spiking neuronal networks spends the major part of the time in the phase where spikes have arrived at a compute node and need to be delivered to their target neurons. These spikes were emitted over the last interval between communication steps by source neurons distributed across many compute nodes and are inherently irregular and unsorted with respect to their targets. For finding those targets, the spikes need to be dispatched to a three-dimensional data structure with decisions on target thread and synapse type to be made on the way.

Modeling Patient-Specific Magnetic Drug Targeting Within the Intracranial Vasculature.

Drug targeting promises to substantially enhance future therapies, for example through the focussing of chemotherapeutic drugs at the site of a tumor, thus reducing the exposure of healthy tissue to unwanted damage. Promising work on the steering of medication in the human body employs magnetic fields acting on nanoparticles made of paramagnetic materials. We develop a computational tool to aid in the optimization of the physical parameters of these particles and the magnetic configuration, estimating the fraction of particles reaching a given target site in a large patient-specific vascular system for different physiological states (heart rate, cardiac output, etc.

Comparative analysis of multiple genome-scale data sets.

The ongoing analyses of published genome-scale data sets is evidence that different approaches are required to completely mine this data. We report the use of novel tools for both visualization and data set comparison to analyze yeast gene-expression (cell cycle and exit from stationary phase/G(0)) and protein-interaction studies. This analysis led to new insights about each data set.