Evolutionary conservation of midline axon guidance activity between and Frazzled.

The regulation of midline crossing of axons is of fundamental importance for the proper development of nervous system connectivity in bilaterian animals. A number of conserved axon guidance signaling pathways coordinate to attract or repel axons at the nervous system midline to ensure the proper regulation of midline crossing. The attractive Netrin-Frazzled/DCC (Net-Fra) signaling pathway is widely conserved among bilaterians, but it is not clear whether the mechanisms by which Net and Fra promote midline crossing are also conserved.

Ultra-fast single-crystal CVD diamonds in the particle time-of-flight (PTOF) detector for low yield burn-history measurements on the NIF (invited).

The Particle Time of Flight (PTOF) diagnostic is a chemical vapor deposition diamond-based detector and is the only diagnostic for measuring nuclear bang times of low yield (<1013) shots on the National Ignition Facility. Recently, a comprehensive study of detector impulse responses revealed certain detectors with very fast and consistent impulse responses with a rise time of <50 ps, enabling low yield burn history measurements. At the current standoff of 50 cm, this measurement is possible with fast 14 MeV neutrons from deuterium-tritium (DT) fusion plasmas.

Do Routine Postoperative Radiographs Influence the Management of Distal Radius Fractures Following Volar Locking Plate Fixation?

Background: Distal radius fractures (DRFs) are a common orthopaedic injury, often requiring surgical intervention. Routine postoperative radiographs are frequently obtained after surgical fixation to ensure adequacy of fixation and rule out early complications, yet their necessity remains unclear. Through this study, we tried to evaluate the impact of routine postoperative radiographs on the management of DRFs.

Denoising low-field MR images with a deep learning algorithm based on simulated data from easily accessible open-source software.

In this study, we introduce a denoising method aimed at improving the contrast ratio in low-field MRI (LFMRI) using an advanced 3D deep convolutional residual network model. Our approach employs synthetic brain imaging datasets that closely mimic the contrast and noise characteristics of LFMRI scans, addressing the limitation of available in-vivo LFMRI datasets for training deep learning models. In the simulation data, the Relative Contrast Ratio (RCR) increased, and similar improvements were observed in the in-vivo data across different imaging conditions.