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Here, recognizing the recognizing the growing threat of non-biodegradable plastic waste, the authors investigated the ability to use a modified enzyme identified in bacteria to decompose polyethylene terephthalate (PET). They used simulations to screen and identify an optimized enzyme based on machine learning models. Ultimately, they identified a potential mutant PETases capable of decomposing PET with improved thermal stability.

In this study, the authors tested whether the compound bromelain extracted from pineapples could protect skin cells from UV damage.

Here, the authors recognized the tendency of bacteria to form biofilms, where this behavior offers protection against threats such as antibiotics. To investigate this, they observed the effects of sublethal exposure of the antibiotic ampicillin on E. coli biofilm formation with an optical density crystal violet assay. They found that exposure to ampicillin resulted in the favored formation of biofilms over time, as free-floating bacteria were eradicated.

In this study, the authors test whether excess copper exposure has neurobehavioral effects on Hirudo verbana leeches.

The authors investigated prophages present in Streptococcus bacteria that may increase their survival in different environments.

In this study, the authors investigate the effects of acidity on the survival of commercial probiotic Lovebug bacterial strains.

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

The authors looked at previous studies to evaluate the ability to use serum levels of certain cytokines as biomarkers for pneumoconiosis.

Here, seeking to understand the effects of emotion on memory recall, the authors used a study of 30 teenagers, comparing their ability to recall details from information or narrative writing. They found improved recall of narrative writing, suggesting emotional response can contribute to improved memory recall.

In this study, the authors developed and assessed the accuracy of a machine learning algorithm to identify skin cancers using images of biopsies.