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Artificial intelligence assisted violin performance learning

Zhang et al. | Aug 30, 2023

Artificial intelligence assisted violin performance learning
Image credit: Philip Myrtorp

In this study the authors looked at the ability of artificial intelligence to detect tempo, rhythm, and intonation of a piece played on violin. Technology such as this would allow for students to practice and get feedback without the need of a teacher.

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The impact of genetic analysis on the early detection of colorectal cancer

Agrawal et al. | Aug 24, 2023

The impact of genetic analysis on the early detection of colorectal cancer

Although the 5-year survival rate for colorectal cancer is below 10%, it increases to greater than 90% if it is diagnosed early. We hypothesized from our research that analyzing non-synonymous single nucleotide variants (SNVs) in a patient's exome sequence would be an indicator for high genetic risk of developing colorectal cancer.

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Development of anti-cancer bionanoparticles isolated from corn for bone cancer treatment

Richardson et al. | Apr 20, 2023

Development of anti-cancer bionanoparticles isolated from corn for bone cancer treatment

This study hypothesizes that nanoparticles derived from corn (cNPs)may have anti-proliferative effects on bone cancer and metastasized bone cancer. It finds that human osteosarcoma and human lung carcinoma metastasized to bone marrow cell viability decreased to 0% when treated with cNPs. Overall, these results indicate that cNPs have anti-proliferative effects on bone cancer cells and cancer cells that metastasize to the bone.

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Employee resignation study in Fairfax County

Zhang et al. | Mar 03, 2023

Employee resignation study in Fairfax County

In this study, the authors address potential reasons why employees may voluntarily resign. This is in response to the currently observed economic trend The Great Resignation. Through analysis of federal and local government data along with survey results from Fairfax County, they concluded that adding additional benefits will help companies retain talented empolyees.

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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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