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Machine learning-based enzyme engineering of PETase for improved efficiency in plastic degradation

Gupta et al. | Jan 31, 2023

 Machine learning-based enzyme engineering of PETase for improved efficiency in plastic degradation
Image credit: Markus Spiske

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.

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Differences in Reliability and Predictability of Harvested Energy from Battery-less Intermittently Powered Systems

Sampath et al. | Apr 29, 2020

Differences in Reliability and Predictability of Harvested Energy from Battery-less Intermittently Powered Systems

Solar and radio frequency harvesters serve as a viable alternative energy source to batteries in many cases where the battery cannot be easily replaced. Using specifically designed circuit models, the authors quantify the reliability of different harvested energy sources to identify the most practical and efficient forms of renewable energy.

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Analysis of Technology Usage of Teens: Correlating Social Media, Technology Use, Participation in Sports, and Popularity

Düzgezen et al. | Mar 27, 2020

Analysis of Technology Usage of Teens: Correlating Social Media, Technology Use, Participation in Sports, and Popularity

Social media usage is predicted to impact teen well-being and emotional status. This study sought to assess the impact of teen technology usage on their social lives. Surveys of 8th and 9th graders were used to assess compare technology usage between males and females as well as and how social media usage impacts the perception of social environment at school.

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Assessing and Improving Machine Learning Model Predictions of Polymer Glass Transition Temperatures

Ramprasad et al. | Mar 18, 2020

Assessing and Improving Machine Learning Model Predictions of Polymer Glass Transition Temperatures

In this study, the authors test whether providing a larger dataset of glass transition temperatures (Tg) to train the machine-learning platform Polymer Genome would improve its accuracy. Polymer Genome is a machine learning based data-driven informatics platform for polymer property prediction and Tg is one property needed to design new polymers in silico. They found that training the model with their larger, curated dataset improved the algorithm's Tg, providing valuable improvements to this useful platform.

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Utilizing a Wastewater-Based Medium for Engineered Saccharomyces cerevisiae for the Biological Production of Fatty Alcohols and Carboxylic Acids to Replace Petrochemicals

Ramesh et al. | Oct 02, 2019

Utilizing a Wastewater-Based Medium for Engineered <em>Saccharomyces cerevisiae</em> for the Biological Production of Fatty Alcohols and Carboxylic Acids to Replace Petrochemicals

Saccharomyces cerevisiae yeast is used to produce bioethanol, an alternative to fossil fuels. In this study, authors take advantage of this well studied yeast by genetically engineering them to increase fatty acid biosynthesis and culturing in a cost-effective wastewater based medium; potentially providing a sustainable alternative to petrochemicals.

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Cytokine Treatment for Myocarditis May Directly Impact Cardiomyocytes Negatively

Kasner et al. | Apr 26, 2019

Cytokine Treatment for Myocarditis May Directly Impact Cardiomyocytes Negatively

The purpose of our study was to determine if direct administration of CXCL1/KC to cardiomyocytes causes negative changes to cell density or proliferation. This molecule has been shown to reduce inflammation in certain instances. Homocysteine models the direct effect of an inflammatory agent on cardiomyocytes. Our question was whether these molecules directly impact cell density through an interaction with the cell proliferation process. We hypothesized that cells treated with CXCL1/KC would maintain the same cell density as untreated cells. In contrast, cells treated with Homocysteine or both Homocysteine and CXCL1/KC, were expected to have a higher cell density that than that of untreated cells.

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