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Bacterial Richness of Soil Samples from Southern New Hampshire

Chalasani et al. | Sep 21, 2016

Bacterial Richness of Soil Samples from Southern New Hampshire

Advancement in DNA sequencing technology has greatly increased our understanding about the role of bacteria in soil. The authors of this study examine the microbial content of soil samples taken from three locations in southern New Hampshire with varying pH and plant composition.

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Mapping the Electromagnetic Field in Front of a Microwave Oven

Xiang et al. | Sep 21, 2019

Mapping the Electromagnetic Field in Front of a Microwave Oven

There is limited evidence that extended exposure to an electromagnetic field (EMF) has negative health effects on humans. The authors measured the power density and strength of EMF at different distances and directions in front of a microwave oven, and they discuss the safety of different distances.

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Blockchain databases: Encrypted for efficient and secure NoSQL key-store

Mehrota et al. | Mar 18, 2023

Blockchain databases: Encrypted for efficient and secure NoSQL key-store
Image credit: Ayushi Mehrota & David Kim

Although commonly associated with cryptocurrency, blockchains offer security that other databases could benefit from. These student authors tested a blockchain database framework, and by tracking runtime of four independent variables, they prove this framework is feasible for application.

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Peptidomimetics Targeting the Polo-box Domain of Polo-like Kinase 1

Jang et al. | Aug 19, 2016

Peptidomimetics Targeting the Polo-box Domain of Polo-like Kinase 1

Polo-like kinase 1 (Plk1) is a master regulator of mitosis, initiating key steps of cell cycle regulation, and its overexpression is associated with certain types of cancer. In this study, the authors carefully designed peptides that were able to bind to Plk1 at a location that is important for its proper localization and function. Future studies could further develop these peptides to selectively target Plk1 in cancer cells and induce mitotic arrest.

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Enhancing marine debris identification with convolutional neural networks

Wahlig et al. | Apr 03, 2024

Enhancing marine debris identification with convolutional neural networks
Image credit: The authors

Plastic pollution in the ocean is a major global concern. Remotely Operated Vehicles (ROVs) have promise for removing debris from the ocean, but more research is needed to achieve full effectiveness of the ROV technology. Wahlig and Gonzales tackle this issue by developing a deep learning model to distinguish trash from the environment in ROV images.

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