Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks.

Background: Despite the integral role of cephalometric analysis in orthodontics, there have been limitations regarding the reliability, accuracy, etc. of cephalometric landmarks tracing. Attempts on developing automatic plotting systems have continuously been made but they are insufficient for clinical applications due to low reliability of specific landmarks.

Coronary artery decision algorithm trained by two-step machine learning algorithm.

A two-step machine learning (ML) algorithm for estimating both fractional flow reserve (FFR) and decision (DEC) for the coronary artery is introduced in this study. The primary purpose of this model is to suggest the possibility of ML-based FFR to be more accurate than the FFR calculation technique based on a computational fluid dynamics (CFD) method. For this purpose, a two-step ML algorithm that considers the flow characteristics and biometric features as input features of the ML model is designed.

LC50 Determination of tert-Butyl Acetate using a Nose Only Inhalation Exposure in Rats.

tert-Butyl acetate (TBAc) is an organic solvent, which is commonly used in architectural coatings and industrial solvents. It has recently been exempted from the definition of a volatile organic compound (VOC) by the Air Resources Board (ARB) . Since the use of TBAc as a substitute for other VOCs has increased, thus its potential risk in humans has also increased.