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Berberine, a natural product alkaloid, and its analogs have a wide range of medicinal properties, including antibacterial and anticancer effects. Here, the authors explored a library of alkyl or aryl berberine analogs to probe binding to double-stranded and G-quadruplex DNA. They determined that the nature of the substituent, the position of the substituent, and the nucleic acid target affect the free energy of binding of berberine analogs to DNA and G-quadruplex DNA, however berberine analogs did not result in net stabilization of G-quadruplex DNA.

In this study, the authors determined whether tautomerization dynamics in protic and aprotic solvents displayed differences in reaction rates and in the proportion of the keto and enol tautomers present.

The authors computationally screened potential ALDH1 inhibitors, for use as potential cancer therapeutics.

In this study, the authors investigate whether phototaxis and odortaxis in Drosophila melanogaster occurs through linear summation of cues including light and attractive odorants.

This study examined the impact of erosion on the performance of a triangular aerofoil at a low Reynolds number (Re = 10,000), relevant for harsh conditions like those on Mars.

In this study, the impact of fluorination of heterocyclic drug Allopurinol on its stability and other chemical properties is investigated.

Jazz music is a unique American art form that has spread around the world. Iyer and Iyer study this spread through a computational sociology project examining how jazz popularity is correlated with postmaterialism (an ideology that values self-expression) and political activity.

Quantum computers can perform computational tasks beyond the capability of classical computers, such as simulating quantum systems in materials science and chemistry. Quantum teleportation is the transfer of quantum information across distances, relying on entangled states generated by quantum computing. We sought to mitigate the error of quantum teleportation which was simulated on IBM cloud quantum computers.

With molecular energy being an integral element to the study of molecules and molecular interactions, computational methods to determine molecular energy are used for the preservation of time and resources. However, these computational methods have high demand for computer resources, limiting their widespread feasibility. The authors of this study employed machine learning to address this disadvantage, utilizing neural networks trained on different representations of molecules to predict molecular properties without the requirement of computationally-intensive processing. In their findings, the authors determined the Feedforward Neural Network, trained by two separate models, as capable of predicting molecular energy with limited prediction error.

The authors looked at different ways to build computational resources that would analyze shooting form for basketball players.