In this report, Bhardwaj and Sharma tested whether placing specific plants indoors can reduce levels of indoor air pollution that can lead to lung-related illnesses. Using machine learning, they show that plants improved overall indoor air quality and reduced levels of particulate matter. They suggest that plant-based interventions coupled with sensors may be a useful long-term solution to reducing and maintaining indoor air pollution.
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Computational Structure-Activity Relationship (SAR) of Berberine Analogs in Double-Stranded and G-Quadruplex DNA Binding Reveals Both Position and Target Dependence
Berberine, a natural product alkaloid, and its analogs have a wide range of medicinal properties, including antibacterial and anticancer effects. Here, the authors explored a library of alkyl or aryl berberine analogs to probe binding to double-stranded and G-quadruplex DNA. They determined that the nature of the substituent, the position of the substituent, and the nucleic acid target affect the free energy of binding of berberine analogs to DNA and G-quadruplex DNA, however berberine analogs did not result in net stabilization of G-quadruplex DNA.
Read More...Luteolin's positive inhibition of melanoma cell lines.
Luteolin (3′,4′,5,7-tetrahydroxyflavone) is a flavonoid that occurs in fruits, vegetables, and herbs. Research suggests that luteolin is effective against various forms of cancer by triggering apoptosis pathways. This experiment analyzes the effects of luteolin on the cell viability of malignant melanoma cells using an in vitro experiment to research alternative melanoma treatments and hopefully to help further cancer research as a whole.
Read More...The Effect of Statement Biased Popular Media Consumption on Public Perceptions of Nuclear Power
The authors investigate the effects of popular media consumption on the public's opinion on nuclear power. They find that regardless of education level or positive/negative bias of the article, participants are willing to modify their opinions on nuclear power after consuming a single article.
Read More...Overcoming The Uncanny Valley Through Shared Stressful Experience with a Humanoid Robot
The "Uncanny Valley" is a phenomenon in which humans feel discomfort in the presence of objects that are almost, but not quite, human-like. In this study, the authors tested whether this phenomenon could be overcome by sharing a stressful experience with a humanoid robot. They found that human subjects more readily accepted a robot partner that they had previously shared a stressful experience with, suggesting a potential method for increasing the effectiveness of beneficial human-robot interactions by reducing the Uncanny Valley effect.
Read More...Contrast-Enhanced Magnetic Resonance Imaging at Earth’s Magnetic Field Using Trace Gd3+ and Ho3+ Salts
In this study, the authors explore contrast-enhanced magnetic resonance imaging at Earth's field.
Read More...Isolation of Microbes From Common Household Surfaces
Microorganisms such as bacteria and fungi live everywhere in the world around us. The authors here demonstrate that these predominantly harmless microbes can be isolated from many household locations that appear "clean." Further, they test the cleaning power of 70% ethanol and suggest that many "clean" surfaces are not in fact "sterile."
Read More...Examining the prevalence of depression in coronary artery disease patients: a cross-sectional analysis
The authors surveyed individuals diagnosed with coronary artery disease about their mental health to study a potential connection between coronary artery disease and depression.
Read More...Cardiovascular Disease Prediction Using Supervised Ensemble Machine Learning and Shapley Values
The authors test the effectiveness of machine learning to predict onset of cardiovascular disease.
Read More...Entropy-based subset selection principal component analysis for diabetes risk factor identification
In this article, the authors looked at developing a strategy that would allow for earlier diagnosis of Diabetes as that improves long-term outcomes. They were able to find that BMI, tricep skin fold thickness, and blood pressure are the risk factors with the highest accuracy in predicting diabetes risk.
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