Laparoscopic repair of a large interstitially incarcerated inguinal hernia.

A 68 year old female presented for elective repair of an abdominal wall hernia. Preoperative CT imaging revealed a right inguinal hernia defect with hernia contents coursing cephalad between the external and internal abdominal oblique muscles. This was consistent with an interstitial inguinal hernia, a rare entity outside of post- traumatic hernias. At operation the hernia contents were reduced laparoscopically. The hernia was then repaired by transitioning to the totally extraperitoneal (TEP) approach using a 15cm X 15cm piece of polyester mesh. The patient had an uneventful recovery. Interstitial hernias are rare, difficult to diagnose and potentially dangerous if left untreated. There is no consensus on the ideal repair of these unique hernias. This represents a minimally invasive repair of an unusual hernia, with a novel approach to diagnose and manage the hernia and its redundant sac

A multivariate semiparametric Bayesian spatial modeling framework for hurricane surface wind fields

Storm surge, the onshore rush of sea water caused by the high winds and low pressure associated with a hurricane, can compound the effects of inland flooding caused by rainfall, leading to loss of property and loss of life for residents of coastal areas. Numerical ocean models are essential for creating storm surge forecasts for coastal areas. These models are driven primarily by the surface wind forcings. Currently, the gridded wind fields used by ocean models are specified by deterministic formulas that are based on the central pressure and location of the storm center. While these equations incorporate important physical knowledge about the structure of hurricane surface wind fields, they cannot always capture the asymmetric and dynamic nature of a hurricane. A new Bayesian multivariate spatial statistical modeling framework is introduced combining data with physical knowledge about the wind fields to improve the estimation of the wind vectors. Many spatial models assume the data follow a Gaussian distribution. However, this may be overly-restrictive for wind fields data which often display erratic behavior, such as sudden changes in time or space. In this paper we develop a semiparametric multivariate spatial model for these data. Our model builds on the stick-breaking prior, which is frequently used in Bayesian modeling to capture uncertainty in the parametric form of an outcome. The stick-breaking prior is extended to the spatial setting by assigning each location a different, unknown distribution, and smoothing the distributions in space with a series of kernel functions. This semiparametric spatial model is shown to improve prediction compared to usual Bayesian Kriging methods for the wind field of Hurricane Ivan.Comment: Published at http://dx.doi.org/10.1214/07-AOAS108 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Rapport betreffende de slechte landbouwkundige toestand van een opgevulde holle weg

In de ruilverkaveling 'Munstergeleen-Schinveld' is een holle weg opgevuld. Deze doorsnijdt twee kavels. De gebruikers van de twee kavels hebben blijkbaar op verschillende wijze getracht, de nadelen van de wateroverlast te beperken. Oorzaken van wateroverlast komen in deze studier aan bod

Single-Walled Carbon Nanotubes as Shadow Masks for Nanogap Fabrication

We describe a technique for fabricating nanometer-scale gaps in Pt wires on insulating substrates, using individual single-walled carbon nanotubes as shadow masks during metal deposition. More than 80% of the devices display current-voltage dependencies characteristic of direct electron tunneling. Fits to the current-voltage data yield gap widths in the 0.8-2.3 nm range for these devices, dimensions that are well suited for single-molecule transport measurements

Employment protection, technology choice, and worker allocation

Using a country-industry panel dataset (EUKLEMS) we uncover a robust empirical regularity, namely that high-risk innovative sectors are relatively smaller in countries with strict employment protection legislation (EPL). To understand the mechanism, we develop a two-sector matching model where firms endogenously choose between a safe technology with known productivity and a risky technology with productivity subject to sizeable shocks. Strict EPL makes the risky technology relatively less attractive because it is more costly to shed workers upon receiving a low productivity draw. We calibrate the model using a variety of aggregate, industry and micro-level data sources. We then simulate the model to reflect both the observed differences across countries in EPL and the observed increase since the mid-1990s in the variance of firm performance associated with the adoption of information and communication technology. The simulations produce a differential response to the arrival of risky technology between low- and high-EPL countries that coincides with the findings in the data. The described mechanism can explain a considerable portion of the slowdown in productivity in the EU relative to the US since 1995

Unexpected Scaling of the Performance of Carbon Nanotube Transistors

We show that carbon nanotube transistors exhibit scaling that is qualitatively different than conventional transistors. The performance depends in an unexpected way on both the thickness and the dielectric constant of the gate oxide. Experimental measurements and theoretical calculations provide a consistent understanding of the scaling, which reflects the very different device physics of a Schottky barrier transistor with a quasi-one-dimensional channel contacting a sharp edge. A simple analytic model gives explicit scaling expressions for key device parameters such as subthreshold slope, turn-on voltage, and transconductance.Comment: 4 pages, 4 figure