Browse Articles

In this study, the authors investigate the effects of acetone on the color of copper chloride (CuCl₂) solution, which has important implications for detecting copper in the environment.

In this study the authors looked the trajectories of cosmic rays moving through a dipole field. They found that the trajectories of cosmic rays are determined by a particle's energy and interaction with Earth's B field.

A bottleneck in deleting algal blooms is that current data section is manual and is reactionary to an existing algal bloom. These authors made a custom-designed Seek and Destroy Algal Mitigation System (SDAMS) that detects harmful algal blooms at earlier time points with astonishing accuracy, and can instantaneously suppress the pre-bloom algal population.

The authors looked at how a 3D bioprinter could be used to model the blood brain barrier.

Berberine is a natural quaternary alkaloid that has anti-microbial and anti-cancer effects. This compound can bind to Guanine Quadruplex (G4) DNA secondary complexes to help inhibit cancer cell proliferation. In this study, the authors investigate whether incorporating large aromatic rings helps to stabilize berberine-G4 interactions.

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.

In this study, the authors tested the ability and accuracy of a neural net to identify patterns in complex number matrices.

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

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.