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In this article, the authors explored the locomotory movement of Euglena sp. and Tetrahymena pyriformis in response to light. Such research bears relevance to the migration and distribution patterns of both T. pyriformis and Euglena as they differ in their method of finding sustenance in their native environments. With little previous research done on the exploration of a potential response to photostimulation enacted by T. pyriformis, the authors found that T. pyriformis do not bias in distribution towards areas of light - unlike Euglena, which displayed an increased prevalence in areas of light.

Semiconductors are the center of the fourth industrial revolution as they are key components for all electronics. Exposed wafers made of silicon (Si), which can easily oxidize, convert to silicon dioxide (SiO₂). The surface of SiO₂ wafers consists of many Si-OH bonds, allowing them to easily bond with water, resulting in a “wet” or hydrophilic condition. We sought to determine a way to modify the surface of SiO₂ wafers to become hydrophobic to ensure safe wet cleaning.

Plant diseases can cause up to 50% crop yield loss for the popular tomato plant. A mobile device-based method to identify diseases from photos of symptomatic leaves via computer vision can be more effective due to its convenience and accessibility. To enable a practical mobile solution, a “shallow” convolutional neural networks (CNNs) with few layers, and thus low computational requirement but with high accuracy similar to the deep CNNs is needed. In this work, we explored if such a model was possible.

Seeking to investigate the effects of ambient pollutants on human respiratory health, here the authors used machine learning to examine asthma in Lost Angeles County, an area with substantial pollution. By using machine learning models and classification techniques, the authors identified that nitrogen dioxide and ozone levels were significantly correlated with asthma hospitalizations. Based on an identified seasonal surge in asthma hospitalizations, the authors suggest future directions to improve machine learning modeling to investigate these relationships.

In this study, the authors explore contrast-enhanced magnetic resonance imaging at Earth's field.

Due to the increase in antimicrobial resistance, alternative medicinal therapies are being explored. Studies have shown that honey and ginger alone have antimicrobial effects on the genera Staphylococcus and Escherichia, including S. epidermidis and E. coli. The authors of this study tested whether a honey-ginger supplement, Jengimiel™, could be used as an antimicrobial agent against S. epidermidis and E. coli K-12.

Here the authors used morphological characters and DNA barcoding to identify arthropods found within a residential house. With this method they identified their species and compared them against pests lists provided by the US government. They found that none of their identified species were considered to be pests providing evidence against the misconception that arthropods found at home are harmful to humans. They suggest that these methods could be used at larger scales to better understand and aid in mapping ecosystems.

In this study, the authors determine optimal pH levels for maximizing isopropanol degradation in water. This has important applications for cleaning up polluted wastewater in the environment.

Many weight loss supplements contain the stimulant caffeine, but do not disclose the amount. Here, authors measure and compare the amount of caffeine in different dietary supplements. This research gives consumers better understanding of the impact natural supplements may have on their health.

In this study, the authors assess the antioxidant properties of vitamins A, C and E given to mustard plants after oxidative damage. This research shows an interesting comparison of the vitamins' effect on plant recovery and health.