Synthesis of Esters of Ginsenoside Metabolite M1 and Their Cytotoxicity on MGC80-3 Cells

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First-Principles Investigation of Anistropic Hole Mobilities in Organic Semiconductors

We report a simple first-principles-based simulation model (combining quantum mechanics with Marcus−Hush theory) that provides the quantitative structural relationships between angular resolution anisotropic hole mobility and molecular structures and packing. We validate that this model correctly predicts the anisotropic hole mobilities of ruberene, pentacene, tetracene, 5,11-dichlorotetracene (DCT), and hexathiapentacene (HTP), leading to results in good agreement with experiment

Image Enhancement for Tracking the Translucent Larvae of Drosophila melanogaster

Drosophila melanogaster larvae are model systems for studies of development, synaptic transmission, sensory physiology, locomotion, drug discovery, and learning and memory. A detailed behavioral understanding of larvae can advance all these fields of neuroscience. Automated tracking can expand fine-grained behavioral analysis, yet its full potential remains to be implemented for the larvae. All published methods are unable to track the larvae near high contrast objects, including the petri-dish edges encountered in many behavioral paradigms. To alleviate these issues, we enhanced the larval contrast to obtain complete tracks. Our method employed a dual approach of optical-contrast boosting and post-hoc image processing for contrast enhancement. We reared larvae on black food media to enhance their optical contrast through darkening of their digestive tracts. For image processing we performed Frame Averaging followed by Subtraction then Thresholding (FAST). This algorithm can remove all static objects from the movie, including petri-dish edges prior to processing by the image-tracking module. This dual approach for contrast enhancement also succeeded in overcoming fluctuations in illumination caused by the alternating current power source. Our tracking method yields complete tracks, including at the edges of the behavioral arena and is computationally fast, hence suitable for high-throughput fine-grained behavioral measurements