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The authors looked at the impacts of short-chain fatty acids on muscle fiber formation as well as insulin sensitivity using a model of mouse myoblasts.

Here the authors aimed to understand factors influencing adolescent fentanyl exposure, hypothesizing a positive association between social media usage, socioeconomic factors, and fentanyl abuse among U.S. teens. Their analysis of the Monitoring the Future dataset revealed that a history of suspension and use of marijuana or alcohol were linked to higher fentanyl use, and while not statistically significant, a notable positive correlation between social media use and fentanyl frequency was observed.

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

Default opt-ins and social proof tags effect on decision making in an e-commerce context

Robot-assisted minimally invasive surgery (RMIS) benefits from increased precision and faster recovery, with force feedback from the surgical tool being critical for control. Researchers tested the use of neural networks for detecting the vanishing point of the tool, a key element for force feedback.

The authors compare air quality in the presence and absence of a living wall in a high school hallway in Brooklyn, NY.

The authors looked at egg laying rates in chickens at two different small farms. They found when the chickens were provided homemade, colorful toys in their environment that their egg production rates increased.

The authors compare rates of renewable energy adoption between states that historically vote for democrats versus republicans in presidential elections.

Arsenic contamination in rice, caused by the use of arsenic-laden groundwater for irrigation, is a growing global concern, affecting over 150 million people. To address this, researchers hypothesized that genetically modifying rice plants with arsenic-resistant genes could reduce arsenic uptake and allow the plants to detoxify arsenic, making them safer to consume.

This article describes the classification of medical text data using vector databases and text embedding. Various large language models were used to generate this medical data for the classification task.