Gary Peltz, Jeffrey Glenn, and colleagues report that a pre-clinical mouse toxicology model can detect liver toxicity of a drug that caused liver failure in several early clinical trial participants in 1993. Please see later in the article for the Editors' Summary

Recent attention has been given to mesoscale phenomena across geospace (∼10 s km to 500 km in the ionosphere or ∼0.5 RE to several RE in the magnetosphere), as their contributions to the system global response are important yet remain uncharacterized mostly due to limitations in data resolution and coverage as well as in computational power. As data and models improve, it becomes increasingly valuable to advance understanding of the role of mesoscale phenomena contributions—specifically, in magnetosphere-ionosphere coupling. This paper describes a new method that utilizes the 2D array of Time History of Events and Macroscale Interactions during Substorms (THEMIS) white-light all-sky-imagers (ASI), in conjunction with meridian scanning photometers, to estimate the auroral scale sizes of intense precipitating energy fluxes and the associated Hall conductances. As an example of the technique, we investigated the role of precipitated energy flux and average energy on mesoscales as contrasted to large-scales for two back-to-back substorms, finding that mesoscale aurora contributes up to ∼80% (∼60%) of the total energy flux immediately after onset during the early expansion phase of the first (second) substorm, and continues to contribute ∼30–55% throughout the remainder of the substorm. The average energy estimated from the ASI mosaic field of view also peaked during the initial expansion phase. Using the measured energy flux and tables produced from the Boltzmann Three Constituent (B3C) auroral transport code (Strickland et al., 1976; 1993), we also estimated the 2D Hall conductance and compared it to Poker Flat Incoherent Scatter Radar conductance values, finding good agreement for both discrete and diffuse aurora.

It is well known that noise on medical image resulting in low image quality has largely limited the diagnostic usefulness. Therefore, noise reduction for medical images is significantly urgent. However, most reported noise reduction methods, which are usually based on the local statistics of a medical image, are not efficient for medical image noise reduction. This Paper presents a novel approach for medical image noise reduction using Radon transform (RT) and an adaptive median filter based on Walsh list in the Laplacian pyramid domain. After that, experiments results on artificial and clinical X-ray Computed Tomography (CT) images with Gaussian noise and salt&pepper noise using proposed method and traditional methods have been summarized. Also, speckle reduction on Ultrasound (US) image has been performed using artificial US image. Based on the experimental results, it is concluded that the proposed method showed better capability for noise reduction on medical images compared with other traditional methods.

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