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Flight paths over greenspace in major United States airports

Lee et al. | Sep 26, 2023

Flight paths over greenspace in major United States airports
Image credit: Mostafijur Rahman Nasim

Greenspaces (urban and wetland areas that contain vegetation) are beneficial to reducing pollution, while airplanes are a highly-polluting method of transportation. The authors examine the intersection of these two environmental factors by processing satellite images to reveal what percentage of flight paths go over greenspaces at major US airports.

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Groundwater prediction using artificial intelligence: Case study for Texas aquifers

Sharma et al. | Apr 19, 2024

Groundwater prediction using artificial intelligence: Case study for Texas aquifers

Here, in an effort to develop a model to predict future groundwater levels, the authors tested a tree-based automated artificial intelligence (AI) model against other methods. Through their analysis they found that groundwater levels in Texas aquifers are down significantly, and found that tree-based AI models most accurately predicted future levels.

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Deciphering correlation and causation in risk factors for heart disease with Mendelian randomization

Singh et al. | Feb 08, 2023

Deciphering correlation and causation in risk factors for heart disease with Mendelian randomization
Image credit: Robina Weermeijer

Here, seeking to identify the risk of coronary artery disease (CAD), a major cause of cardiovascular disease, the authors used Mendelian randomization. With this method they identified several traits such as blood pressure readings, LDL cholesterol and BMI as significant risk factors. While other traits were not found to be significant risk factors.

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Methanotrophic bioremediation for the degradation of oceanic methane and chlorinated hydrocarbons

Lee et al. | Oct 08, 2021

Methanotrophic bioremediation for the degradation of oceanic methane and chlorinated hydrocarbons

Seeking an approach to address the increasing levels of methane and chlorinated hydrocarbons that threaten the environment, the authors worked to develop a novel, low-cost biotrickling filter for use as an ex situ method tailored to marine environments. By using methanotrophic bacteria in the filter, they observed methane degradation, suggesting the feasibility of chlorinated hydrocarbon degradation.

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The Cilium- and Centrosome-Associated Protein CCDC11 Is Required for Cytokinesis via Midbody Recruitment of the ESCRT- III Membrane Scission Complex Subunit CHMP2A

Ahmed et al. | Mar 14, 2018

The Cilium- and Centrosome-Associated Protein CCDC11 Is Required for Cytokinesis via Midbody Recruitment of the ESCRT- III Membrane Scission Complex Subunit CHMP2A

In order for cells to successfully multiply, a number of proteins are needed to correctly coordinate the replication and division process. In this study, students use fluorescence microscopy and molecular methods to study CCDC11, a protein critical in the formation of cilia. Interestingly, they uncover a new role for CCDC11, critical in the cell division across multiple human cell lines.

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Talinum paniculatum root exhibits synergistic antimicrobial activity with Tetracycline, Erythromycin, and Streptomycin against S. aureus but has no observed effect on antibiotic efficacy against E. coli

Patel et al. | Jan 09, 2018

Talinum paniculatum root exhibits synergistic antimicrobial activity with Tetracycline, Erythromycin, and Streptomycin against S. aureus but has no observed effect on antibiotic efficacy against E. coli

Patel et al. explore whether T. paniculatum plant extract can work with modern antibiotics to increase antibiotic efficacy against common disease-causing bacteria. The plant extract in conjunction with the antibiotic shows promise in battling S. aureus. The authors present a cost-effective method to increase antibiotic efficacy in a time where antibiotic resistant bacteria is becoming a growing problem.

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Prediction of preclinical Aβ deposit in Alzheimer’s disease mice using EEG and machine learning

Igarashi et al. | Nov 29, 2022

Prediction of preclinical Aβ deposit in Alzheimer’s disease mice using EEG and machine learning

Alzheimer’s disease (AD) is a common disease affecting 6 million people in the U.S., but no cure exists. To create therapy for AD, it is critical to detect amyloid-β protein in the brain at the early stage of AD because the accumulation of amyloid-β over 20 years is believed to cause memory impairment. However, it is difficult to examine amyloid-β in patients’ brains. In this study, we hypothesized that we could accurately predict the presence of amyloid-β using EEG data and machine learning.

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