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Reactivity-informed design, synthesis, and Michael addition kinetics of C-ring andrographolide analogs

Zhou et al. | Nov 17, 2022

Reactivity-informed design, synthesis, and Michael addition kinetics of C-ring andrographolide analogs

Here, based on the identification of androgapholide as a potential therapeutic treatment against cancer, Alzheimer's disease, diabetes, and multiple sclerosis, due to its ability to inhibit a signaling pathway in immune system function, the authors sought ways to optimize the natural product human systems by manipulating its chemical structure. Through the semisynthesis of a natural product along with computational studies, the authors developed an understanding of the kinetic mechanisms of andrographolide and semisynthetic analogs in the context of Michael additions.

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Changing the surface properties of the backside of a silicon wafer to repel oil and prevent particle binding

Choi et al. | Feb 14, 2025

Changing the surface properties of the backside of a silicon wafer to repel oil and prevent particle binding

Wafers, essential in microchip production, can develop issues like leveling problems and wafer slip due to the formation of silanol bonds on their backside, which attract silica particles and oil. Authors tested addressing this issue with a coating of [acetoxy(polyethyleneoxy)propyl]triethoxysilane (APTS) applied to the wafer’s backside, preventing particle binding and oil adherence.

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Rhythmic lyrics translation: Customizing a pre-trained language model using stacked fine-tuning

Chong et al. | May 01, 2023

Rhythmic lyrics translation: Customizing a pre-trained language model using stacked fine-tuning
Image credit: Pixabay

Neural machine translation (NMT) is a software that uses neural network techniques to translate text from one language to another. However, one of the most famous NMT models—Google Translate—failed to give an accurate English translation of a famous Korean nursery rhyme, "Airplane" (비행기). The authors fine-tuned a pre-trained model first with a dataset from the lyrics domain, and then with a smaller dataset containing the rhythmical properties, to teach the model to translate rhythmically accurate lyrics. This stacked fine-tuning method resulted in an NMT model that could maintain the rhythmical characteristics of lyrics during translation while single fine-tuned models failed to do so.

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