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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.

The authors looked at different combinations of risk-adjusted return measures to determine which combination would provide an optimal return for investors. They found that different combinations performed better dependent on investment timeframe.

Artificial sweeteners are rising in popularity, in part due to the influence of social media platforms like YouTube. However, YouTube commenters often repeat information about artificial sweeteners that is not supported by scientific research. To investigate how misinformation about sweeteners spreads through social media, Kim and Yoo conduct a content analysis of YouTube comments to reveal how many comments repeat misinformation about artificial sweeteners' effects.

Here the authors sought to use three machine learning models to predict poverty levels in Cambodia based on available household data. They found teat multilayer perceptron outperformed the other models, with an accuracy of 87 %. They suggest that data-driven approaches such as these could be used more effectively target and alleviate poverty.

Using a convolution neural network, these authors show machine learning can clinically diagnose breast cancer with high accuracy.

Here seeking to identify a possible solution to procrastination among college students, the authors used an online experiment that involved the random assignment of study partners that they shared their study time goal with. These partners were classified by status and group membership. The authors found that status and group membership did not significantly affect the likelihood of college students achieving their committed goals, and also suggest the potential of soft commitment devices that take advantage of social relationships to reduce procrastination.

Acid rain has caused devastating decreases in ecosystems across the globe. To mimic the effect of acid rain on the environment, the authors analyzed the growth of gram-negative (Escherichia coli) and gram-positive (Staphylococcus epidermidis) bacteria in agar solutions with different pH levels. Results show that in a given acidic environment there was a significant decrease in bacterial growth with an increase in vinegar concentration in the agar, suggesting that bacterial growth is impacted by the pH of the environment. Therefore, increased levels of acid rain could potentially harm the ecosystem by altering bacterial growth.

The authors use blood smears from individuals with sickle cell disease to correlate sickle cell frequency with the occurrence of vaso-occlusive crises.

The authors studied the response of various construction materials to UV solar radiation and heat.

The authors investigated alcohol content of foods during preparation and when ready to serve to determine how much alcohol remained in the food.